Effect of particle size on the surface properties and morphology of ground flax

Flax fibers were ground with a ball-mill and four fractions with different size ranges were collected by sieving. These were tested for water sorption, degree of polymerization (DP), copper number, hydroxyl number and analyzed by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM) and inverse gas chromatography (IGC). Significant differences were found between the properties of the flax fiber and those of the ground versions, including fragmentation of fibers, increase of water sorption, copper number, hydroxyl number and surface O/C ratio, and decrease of DP, crystallite size and dispersive component of surface energy (gammasd). Some parameters depended on the particle size: O/C ratio and hydroxyl number had local maxima at 315-630 μm, while gammasd increased steadily with the decrease of particle size. These relationships were explained by fiber disintegration, destruction of waxy surface layer, exposure of cellulosic components, increase of surface area and crystalline imperfections

Surface reconstruction of wear in carpets by using a wavelet edge detector

Carpet manufacturers have wear labels assigned to their products by human experts who evaluate carpet samples subjected to accelerated wear in a test device. There is considerable industrial and academic interest in going from human to automated evaluation, which should be less cumbersome and more objective. In this paper, we present image analysis research on videos of carpet surfaces scanned with a 3D laser. The purpose is obtaining good depth Images for an automated system that should have a high percentage of correct assessments for a wide variety of carpets. The innovation is the use of a wavelet edge detector to obtain a more continuously defined surface shape. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show an improved linear ranking for most carpet types, for two carpet types the results are quite significant

Soft capacitor fibers using conductive polymers for electronic textiles

A novel, highly flexible, conductive polymer-based fiber with high electric capacitance is reported. In its crossection the fiber features a periodic sequence of hundreds of conductive and isolating plastic layers positioned around metallic electrodes. The fiber is fabricated using fiber drawing method, where a multi-material macroscopic preform is drawn into a sub-millimeter capacitor fiber in a single fabrication step. Several kilometres of fibers can be obtained from a single preform with fiber diameters ranging between 500um -1000um. A typical measured capacitance of our fibers is 60-100 nF/m and it is independent of the fiber diameter. For comparison, a coaxial cable of the comparable dimensions would have only ~0.06nF/m capacitance. Analysis of the fiber frequency response shows that in its simplest interrogation mode the capacitor fiber has a transverse resistance of 5 kOhm/L, which is inversely proportional to the fiber length L and is independent of the fiber diameter. Softness of the fiber materials, absence of liquid electrolyte in the fiber structure, ease of scalability to large production volumes, and high capacitance of our fibers make them interesting for various smart textile applications ranging from distributed sensing to energy storage

Developing a Mathematical Model for Bobbin Lace

Bobbin lace is a fibre art form in which intricate and delicate patterns are created by braiding together many threads. An overview of how bobbin lace is made is presented and illustrated with a simple, traditional bookmark design. Research on the topology of textiles and braid theory form a base for the current work and is briefly summarized. We define a new mathematical model that supports the enumeration and generation of bobbin lace patterns using an intelligent combinatorial search. Results of this new approach are presented and, by comparison to existing bobbin lace patterns, it is demonstrated that this model reveals new patterns that have never been seen before. Finally, we apply our new patterns to an original bookmark design and propose future areas for exploration.Comment: 20 pages, 18 figures, intended audience includes Artists as well as Computer Scientists and Mathematician

Markets, Institutions, and the Quality of Agricultural Products: Cotton Quality in India

The modern global textile industry requires cotton with strong and consistent fibers in order to produce high quality goods at the high speeds necessary to recover capital costs. The introduction of high volume instrument (HVI) measurement of cotton fiber quality has strengthened the link between cotton prices and attributes on world markets. The spread of genetically modified (GMO) cotton in India has driven India to the second ranked producer and exporter of cotton in the world. However, contamination and other quality problems are endemic to Indian cotton. Using a unique data set of Indian cotton prices and quality attributes from 5 Indian states, this study uses hedonic price modeling to demonstrate that the linkages between cotton quality and price are weaker in India than they are in the United States.Crop Production/Industries, Production Economics,

Measuring hairiness in carpets by using surface metrology

Recently, an automatic system for grading appearance retention in carpets using our own scanner and image analysis techniques was proposed. A system for carpets with low pile construction and without color patterns has been developed. Appearance changes in carpets with high pile construction were still not well detected. We present an approach based on surface metrology that extract information given by the hairs on the carpet surface. These features are complementary to the texture features previously explored. By combining both features, we expand the use of the automatic grading system including some carpets types with high pile construction

Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means

This work addresses segmentation of volumetric images of woven carbon fiber textiles from micro-tomography data. We propose a semi-supervised algorithm to classify carbon fibers that requires sparse input as opposed to completely labeled images. The main contributions are: (a) design of effective discriminative classifiers, for three-dimensional textile samples, trained on wavelet features for segmentation; (b) coupling of previous step with nonlocal means as simple, efficient alternative to the Potts model; and (c) demonstration of reuse of classifier to diverse samples containing similar content. We evaluate our work by curating test sets of voxels in the absence of a complete ground truth mask. The algorithm obtains an average 0.95 F1 score on test sets and average F1 score of 0.93 on new samples. We conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high-resolution micro-tomography images